Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types

• URL: http://arxiv.org/abs/2005.12762v2
• Date: Wed, 27 May 2020 13:32:15 GMT
• Title: Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
• Authors: Belen Saldias and Deb Roy
• Abstract summary: We introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives. Third, we train a classifier that reaches 84.7% F-score for the highest-agreed clauses. Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.
• Score: 13.350982138577038
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sharing personal narratives is a fundamental aspect of human social behavior as it helps share our life experiences. We can tell stories and rely on our background to understand their context, similarities, and differences. A substantial effort has been made towards developing storytelling machines or inferring characters' features. However, we don't usually find models that compare narratives. This task is remarkably challenging for machines since they, as sometimes we do, lack an understanding of what similarity means. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives (i.e., action, orientation, and evaluation clause types) and train a classifier that reaches 84.7% F-score for the highest-agreed clauses. Finally, we match stories and explore whether people implicitly rely on Labov's framework to compare narratives. We show that actions followed by the narrator's evaluation of these are the aspects non-experts consider the most. Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.

Related papers

• Are Large Language Models Capable of Generating Human-Level Narratives? [114.34140090869175]
  This paper investigates the capability of LLMs in storytelling, focusing on narrative development and plot progression. We introduce a novel computational framework to analyze narratives through three discourse-level aspects. We show that explicit integration of discourse features can enhance storytelling, as is demonstrated by over 40% improvement in neural storytelling.
  arXiv  Detail & Related papers  (2024-07-18T08:02:49Z)
• HEART-felt Narratives: Tracing Empathy and Narrative Style in Personal Stories with LLMs [30.636456219922906]
  Empathy serves as a cornerstone in enabling prosocial behaviors, and can be evoked through sharing of personal experiences in stories. While empathy is influenced by narrative content, intuitively, people respond to the way a story is told as well, through narrative style. We empirically examine and quantify this relationship between style and empathy using LLMs and large-scale crowdsourcing studies.
  arXiv  Detail & Related papers  (2024-05-27T20:00:38Z)
• NarrativePlay: Interactive Narrative Understanding [27.440721435864194]
  We introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives in an immersive environment. We leverage Large Language Models (LLMs) to generate human-like responses, guided by personality traits extracted from narratives. NarrativePlay has been evaluated on two types of narratives, detective and adventure stories, where users can either explore the world or improve their favorability with the narrative characters through conversations.
  arXiv  Detail & Related papers  (2023-10-02T13:24:00Z)
• FaNS: a Facet-based Narrative Similarity Metric [6.992767260794627]
  This paper proposes a novel narrative similarity metric called Facet-based Narrative Similarity (FaNS) FaNS is based on the classic 5W1H facets (Who, What, When, Where, Why, and How) which are extracted by leveraging the state-of-the-art Large Language Models (LLMs)
  arXiv  Detail & Related papers  (2023-09-09T15:29:24Z)
• DeltaScore: Fine-Grained Story Evaluation with Perturbations [69.33536214124878]
  We introduce DELTASCORE, a novel methodology that employs perturbation techniques for the evaluation of nuanced story aspects. Our central proposition posits that the extent to which a story excels in a specific aspect (e.g., fluency) correlates with the magnitude of its susceptibility to particular perturbations. We measure the quality of an aspect by calculating the likelihood difference between pre- and post-perturbation states using pre-trained language models.
  arXiv  Detail & Related papers  (2023-03-15T23:45:54Z)
• M-SENSE: Modeling Narrative Structure in Short Personal Narratives Using Protagonist's Mental Representations [14.64546899992196]
  We propose the task of automatically detecting prominent elements of the narrative structure by analyzing the role of characters' inferred mental state. We introduce a STORIES dataset of short personal narratives containing manual annotations of key elements of narrative structure, specifically climax and resolution. Our model is able to achieve significant improvements in the task of identifying climax and resolution.
  arXiv  Detail & Related papers  (2023-02-18T20:48:02Z)
• Understanding How People Rate Their Conversations [73.17730062864314]
  We conduct a study to better understand how people rate their interactions with conversational agents. We focus on agreeableness and extraversion as variables that may explain variation in ratings.
  arXiv  Detail & Related papers  (2022-06-01T00:45:32Z)
• Computational Lens on Cognition: Study Of Autobiographical Versus Imagined Stories With Large-Scale Language Models [95.88620740809004]
  We study differences in the narrative flow of events in autobiographical versus imagined stories using GPT-3. We found that imagined stories have higher sequentiality than autobiographical stories. In comparison to imagined stories, autobiographical stories contain more concrete words and words related to the first person.
  arXiv  Detail & Related papers  (2022-01-07T20:10:47Z)
• Paragraph-level Commonsense Transformers with Recurrent Memory [77.4133779538797]
  We train a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives. Our results show that PARA-COMET outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
  arXiv  Detail & Related papers  (2020-10-04T05:24:12Z)
• Annotation of Emotion Carriers in Personal Narratives [69.07034604580214]
  We are interested in the problem of understanding personal narratives (PN) - spoken or written - recollections of facts, events, and thoughts. In PN, emotion carriers are the speech or text segments that best explain the emotional state of the user. This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives.
  arXiv  Detail & Related papers  (2020-02-27T15:42:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.